Pub Date : 2024-05-09DOI: 10.1038/s43586-024-00311-9
Nicholas P. Lockyer, Satoka Aoyagi, John S. Fletcher, Ian S. Gilmore, Paul A. W. van der Heide, Katie L. Moore, Bonnie J. Tyler, Lu-Tao Weng
Secondary ion mass spectrometry (SIMS) is a technique for chemical analysis and imaging of solid materials, with applications in many areas of science and technology. It involves bombarding a sample surface under high vacuum with energetic primary ions. The ejected secondary ions undergo mass-to-charge ratio (m/z) analysis and are detected. The resulting mass spectrum contains detailed surface chemical information with sub-monolayer sensitivity. Different experimental configurations provide chemically resolved depth distribution and 2D or 3D images. SIMS is complementary to other surface analysis techniques, such as X-ray photoelectron spectroscopy; chemical imaging techniques, for example, vibrational microspectroscopy methods such as Fourier transform infrared spectroscopy and Raman spectroscopy; and other mass spectrometry imaging techniques, including desorption electrospray ionization and matrix-assisted laser desorption ionization. Features of SIMS include high spatial resolution, high depth resolution and broad chemical sensitivity to all elements, isotopes and molecules up to several thousand mass units. This Primer describes the operating principles of SIMS and outlines how the instrument geometry and operational parameters enable different modes of operation and information to be obtained. Applications, including materials science, surface science, electronic devices, geosciences and life sciences, are explored, finishing with an outlook for the technique. Solid samples can be imaged and chemically analysed using secondary ion mass spectrometry. This Primer describes the secondary ion mass spectrometry experimental setup, in which a primary ion beam sputters secondary ions that are analysed and detected by a mass spectrometer, and explores applications in materials, geological and life sciences.
二次离子质谱法（SIMS）是一种对固体材料进行化学分析和成像的技术，在许多科学和技术领域都有应用。它是在高真空条件下用高能一级离子轰击样品表面。喷射出的二次离子经过质量电荷比（m/z）分析后被检测出来。由此产生的质谱包含详细的表面化学信息，具有亚单层灵敏度。不同的实验配置可提供化学分辨率的深度分布以及二维或三维图像。SIMS 与其他表面分析技术（如 X 射线光电子能谱）、化学成像技术（如傅立叶变换红外光谱和拉曼光谱等振动微光谱方法）以及其他质谱成像技术（包括解吸电喷雾电离和基质辅助激光解吸电离）互为补充。SIMS 的特点包括高空间分辨率、高深度分辨率和广泛的化学灵敏度，可检测高达数千质量单位的所有元素、同位素和分子。本入门指南介绍了 SIMS 的工作原理，并概述了仪器的几何形状和操作参数如何实现不同的操作模式和获取不同的信息。此外，还探讨了 SIMS 的应用，包括材料科学、表面科学、电子设备、地球科学和生命科学，最后对该技术进行了展望。使用二次离子质谱法可以对固体样品进行成像和化学分析。本《入门》介绍了二次离子质谱法的实验装置，其中主离子束喷射出的二次离子由质谱仪进行分析和检测，并探讨了该技术在材料、地质和生命科学领域的应用。
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Pub Date : 2024-05-02DOI: 10.1038/s43586-024-00308-4
Rick A. Homan, John D. Lapek, Christina M. Woo, Sherry Niessen, Lyn H. Jones, Christopher G. Parker
Small molecules can serve as leads for new therapeutics as well as powerful tools to investigate biological processes. Understanding the interactions of these molecules, particularly in native biological environments, is fundamentally critical to their utility. Photoaffinity labelling (PAL) represents one of the few strategies that enable the direct mapping of interactions of small molecules with proteins. PAL uses latent functional groups that form reactive intermediates only upon exposure to light of specific wavelengths that, subsequently, covalently adduct to proximal biomolecules, allowing for their enrichment and identification. Although the original applications of PAL date to six decades ago, the more recent integration with powerful mass spectrometry-based proteomic methods has profoundly impacted the ability to illuminate molecular interactions on a global scale. In this Primer, we discuss the current state-of-the-art of PAL-based strategies for studying molecular interactions in native systems, with a focus on investigations of small molecule–protein interactions. We cover topics including the basic principles of PAL chemistries, PAL probe design, experimental considerations, data analysis and applications of PAL illustrated by recent examples. Finally, we provide our perspective on persistent challenges and our outlook on the field. Photoaffinity labelling (PAL) enables the direct mapping of interactions of small molecules with proteins. In this Primer, Homan et al. discuss the basic principles and considerations involved in the design and implementation of PAL reagents and methods.
小分子既可以作为新疗法的线索，也可以作为研究生物过程的有力工具。了解这些分子的相互作用，尤其是在原生生物环境中的相互作用，对它们的应用至关重要。光亲和标记（PAL）是能够直接绘制小分子与蛋白质相互作用图的少数策略之一。PAL 使用的是潜伏功能基团，只有在特定波长的光照射下才会形成反应中间体，随后与近端生物大分子共价加成，从而实现生物大分子的富集和鉴定。虽然 PAL 的最初应用可追溯到六十年前，但最近与基于质谱的强大蛋白质组学方法的整合，对阐明全球范围内分子相互作用的能力产生了深远影响。在本入门指南中，我们将讨论当前基于 PAL 的原生系统分子相互作用研究策略的最新进展，重点是小分子与蛋白质相互作用的研究。我们讨论的主题包括 PAL 化学的基本原理、PAL 探针设计、实验注意事项、数据分析以及通过最新实例说明的 PAL 应用。最后，我们将对这一领域的长期挑战和前景进行展望。光亲和标记（PAL）可直接绘制小分子与蛋白质相互作用的图谱。在本入门指南中，Homan 等人讨论了设计和实施 PAL 试剂和方法的基本原理和注意事项。
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Pub Date : 2024-05-02DOI: 10.1038/s43586-024-00317-3
This PrimeView highlights the different types of reporter tags that can be paired with photoreactive groups for photoaffinity labeling.
本 PrimeView 着重介绍了可与光活性基团配对用于光亲和标记的不同类型的报告标记。
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Pub Date : 2024-04-25DOI: 10.1038/s43586-024-00307-5
Ulrike G. K. Wegst, Paul H. Kamm, Kaiyang Yin, Francisco García-Moreno
When solutions and slurries are directionally solidified, complex dynamics of solvent crystal growth and solvent templating determine the final hierarchical architecture of the freeze-cast material. With continuous X-ray tomoscopy, it is now possible to study in situ intricate and otherwise elusive ice crystal growth and solvent-templating phenomena. Quantifying these phenomena both time-resolved and in three dimensions provides novel insights into the formation of performance-defining features of freeze-cast cellular solids at several length scales: the material’s pore morphology (first hierarchical level), the molecular, fibrillar and particle self-assembly of components in the cell walls (second level) and the cell wall surface structures (third level). The freeze casting process is attractive because the features of the final hierarchical material architecture — which determine the material’s structural, mechanical and physical properties — can be custom designed for a given application. Overall porosity, pore size, geometry, orientation, particle packing in cell walls and cell wall surface features can be tailored for applications in, for example, biomedicine, environmental engineering, catalysis, power conversion, and energy generation and storage. Freeze casting involves directional solidification of solutions or slurries followed by removal of the solid solvent phase. This Primer introduces the freeze casting technique, including experimental and analysis methods, with a particular focus on using X-ray tomoscopy for real-time, 3D observations of freeze casting dynamics.
当溶液和浆料定向凝固时，溶剂晶体生长和溶剂模板化的复杂动态决定了冻铸材料的最终层次结构。利用连续 X 射线断层扫描技术，现在可以现场研究错综复杂、难以捉摸的冰晶生长和溶剂模板化现象。通过对这些现象进行时间分辨和三维量化，可以在多个长度尺度上对冷冻铸造细胞固体的性能定义特征的形成提供新的见解：材料的孔隙形态（第一个层次），细胞壁中分子、纤维和颗粒成分的自组装（第二个层次）以及细胞壁表面结构（第三个层次）。冷冻铸造工艺之所以具有吸引力，是因为最终分层材料结构的特征--这些特征决定了材料的结构、机械和物理特性--可以针对特定应用进行定制设计。整体孔隙率、孔隙大小、几何形状、取向、细胞壁中的颗粒堆积以及细胞壁表面特征都可以根据生物医学、环境工程、催化、电力转换以及能源生产和储存等应用进行定制。冷冻铸造涉及溶液或浆料的定向凝固，然后去除固体溶剂相。本入门指南介绍了冷冻铸造技术，包括实验和分析方法，尤其侧重于使用 X 射线断层扫描技术对冷冻铸造动态进行实时三维观察。
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Pub Date : 2024-04-25DOI: 10.1038/s43586-024-00316-4
This PrimeView highlights the structures formed with different freeze casting moulds.
该 PrimeView 展示了不同冷冻铸造模具形成的结构。
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Pub Date : 2024-04-11DOI: 10.1038/s43586-024-00310-w
This PrimeView highlights the use of quantitative text analysis in various analytical tasks, from categorizing information to analyzing sentiment and making predictions.
{"title":"Quantitative text analysis","authors":"","doi":"10.1038/s43586-024-00310-w","DOIUrl":"10.1038/s43586-024-00310-w","url":null,"abstract":"This PrimeView highlights the use of quantitative text analysis in various analytical tasks, from categorizing information to analyzing sentiment and making predictions.","PeriodicalId":74250,"journal":{"name":"Nature reviews. Methods primers","volume":" ","pages":"1-1"},"PeriodicalIF":0.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43586-024-00310-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140544572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-11DOI: 10.1038/s43586-024-00302-w
Kristoffer L. Nielbo, Folgert Karsdorp, Melvin Wevers, Alie Lassche, Rebekah B. Baglini, Mike Kestemont, Nina Tahmasebi
Text analysis has undergone substantial evolution since its inception, moving from manual qualitative assessments to sophisticated quantitative and computational methods. Beginning in the late twentieth century, a surge in the utilization of computational techniques reshaped the landscape of text analysis, catalysed by advances in computational power and database technologies. Researchers in various fields, from history to medicine, are now using quantitative methodologies, particularly machine learning, to extract insights from massive textual data sets. This transformation can be described in three discernible methodological stages: feature-based models, representation learning models and generative models. Although sequential, these stages are complementary, each addressing analytical challenges in the text analysis. The progression from feature-based models that require manual feature engineering to contemporary generative models, such as GPT-4 and Llama2, signifies a change in the workflow, scale and computational infrastructure of the quantitative text analysis. This Primer presents a detailed introduction of some of these developments, offering insights into the methods, principles and applications pertinent to researchers embarking on the quantitative text analysis, especially within the field of machine learning. Quantitative text analysis is a range of computational methods to analyse text data statistically and mathematically. In this Primer, Kristoffer Nielbo et al. introduce the methods, principles and applications of the quantitative text analysis across disciplines.
{"title":"Quantitative text analysis","authors":"Kristoffer L. Nielbo, Folgert Karsdorp, Melvin Wevers, Alie Lassche, Rebekah B. Baglini, Mike Kestemont, Nina Tahmasebi","doi":"10.1038/s43586-024-00302-w","DOIUrl":"10.1038/s43586-024-00302-w","url":null,"abstract":"Text analysis has undergone substantial evolution since its inception, moving from manual qualitative assessments to sophisticated quantitative and computational methods. Beginning in the late twentieth century, a surge in the utilization of computational techniques reshaped the landscape of text analysis, catalysed by advances in computational power and database technologies. Researchers in various fields, from history to medicine, are now using quantitative methodologies, particularly machine learning, to extract insights from massive textual data sets. This transformation can be described in three discernible methodological stages: feature-based models, representation learning models and generative models. Although sequential, these stages are complementary, each addressing analytical challenges in the text analysis. The progression from feature-based models that require manual feature engineering to contemporary generative models, such as GPT-4 and Llama2, signifies a change in the workflow, scale and computational infrastructure of the quantitative text analysis. This Primer presents a detailed introduction of some of these developments, offering insights into the methods, principles and applications pertinent to researchers embarking on the quantitative text analysis, especially within the field of machine learning. Quantitative text analysis is a range of computational methods to analyse text data statistically and mathematically. In this Primer, Kristoffer Nielbo et al. introduce the methods, principles and applications of the quantitative text analysis across disciplines.","PeriodicalId":74250,"journal":{"name":"Nature reviews. Methods primers","volume":" ","pages":"1-16"},"PeriodicalIF":0.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43586-024-00302-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140544591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-05DOI: 10.1038/s43586-024-00312-8
René Tanious, Rumen Manolov, Patrick Onghena, Johan W. S. Vlaeyen
Single-case experimental designs are rapidly growing in popularity. This popularity needs to be accompanied by transparent and well-justified methodological and statistical decisions. Appropriate experimental design including randomization, proper data handling and adequate reporting are needed to ensure reproducibility and internal validity. The degree of generalizability can be assessed through replication.
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Pub Date : 2024-04-04DOI: 10.1038/s43586-024-00309-3
This PrimeView highlights how to overcome challenges when performing fluorescence imaging in the second near-infrared window.
本 PrimeView 着重介绍了如何克服在第二个近红外窗口进行荧光成像时遇到的挑战。
{"title":"Near-infrared II fluorescence imaging","authors":"","doi":"10.1038/s43586-024-00309-3","DOIUrl":"10.1038/s43586-024-00309-3","url":null,"abstract":"This PrimeView highlights how to overcome challenges when performing fluorescence imaging in the second near-infrared window.","PeriodicalId":74250,"journal":{"name":"Nature reviews. Methods primers","volume":" ","pages":"1-1"},"PeriodicalIF":0.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43586-024-00309-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140345827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-04DOI: 10.1038/s43586-024-00301-x
Elizabeth Lea Schmidt, Zihao Ou, Erving Ximendes, Han Cui, Carl H. C. Keck, Daniel Jaque, Guosong Hong
Fluorescence imaging in the second near-infrared (NIR-II) window enables deep-tissue imaging with high resolution and improved contrast by taking advantage of the reduced light scattering and tissue autofluorescence in this region of the spectrum. NIR-II fluorescence imaging uses photoluminescent contrast agents — including carbon nanotubes, quantum dots, rare earth-doped nanocrystals, gold nanoclusters, small molecules and their aggregates — and fluorescent proteins, which all exhibit fluorescence in the 1,000–3,000 nm range. After administration of these fluorophores in vivo, live animals can be imaged with specialized detectors and optical instruments, yielding images with contrast and resolution unparalleled by conventional visible and near-infrared fluorescence imaging. This powerful approach enables dynamic imaging of vascular structures and haemodynamics; molecular imaging and image-guided surgery of tumours; and visualization of deep-seated structures, such as the gastrointestinal system. NIR-II fluorescence imaging has revolutionized biomedical imaging over the past 15 years and is poised to make comparable advancements in cardiology, neurobiology and gastroenterology. This Primer describes the principles of NIR-II fluorescence imaging, reviews the most used fluorophores, outlines implementation approaches and discusses specific scientific and clinical applications. Furthermore, the limitations of NIR-II fluorescence imaging are addressed and future opportunities across various scientific domains are explored. Deep tissues can be imaged with high resolution and greater contrast by performing fluorescence imaging in the second near-infrared (NIR-II) window. This Primer summarizes how NIR-II fluorescence imaging can be used in animal models, exploring commonly used fluorophores and implementation approaches across a range of scientific and clinical applications.
利用第二近红外（NIR-II）窗口的荧光成像技术，可以利用该光谱区域较低的光散射和组织自发荧光，进行高分辨率和高对比度的深部组织成像。近红外-II 荧光成像使用的光致发光造影剂包括碳纳米管、量子点、掺稀土纳米晶体、金纳米团簇、小分子及其聚集体和荧光蛋白，它们都在 1000-3000 纳米范围内发出荧光。在体内施用这些荧光团后，活体动物可以通过专门的探测器和光学仪器成像，从而获得对比度和分辨率都无与伦比的传统可见光和近红外荧光成像图像。这种强大的方法可实现血管结构和血液动力学的动态成像；肿瘤的分子成像和图像引导手术；以及胃肠系统等深层结构的可视化。近红外-II荧光成像技术在过去15年中彻底改变了生物医学成像技术，并有望在心脏病学、神经生物学和胃肠病学领域取得类似进展。本入门指南介绍了近红外 II 荧光成像的原理，回顾了最常用的荧光团，概述了实施方法，并讨论了具体的科学和临床应用。此外，还讨论了近红外-II 荧光成像的局限性，并探讨了各个科学领域的未来机遇。通过在第二个近红外（NIR-II）窗口进行荧光成像，可以对深部组织进行高分辨率和高对比度成像。本入门指南总结了如何在动物模型中使用近红外-II 荧光成像，探讨了一系列科学和临床应用中常用的荧光团和实施方法。
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